code: def deploy_config(
      model,
      initial_instance_count,
      instance_type,
      endpoint_name=None,
      tags=None,
  ):
      """Export Airflow deploy config from a SageMaker model
  
      Args:
          model (sagemaker.model.Model): The SageMaker model to export the Airflow
              config from.
          initial_instance_count (int): The initial number of instances to run in
              the ``Endpoint`` created from this ``Model``.
          instance_type (str): The EC2 instance type to deploy this Model to. For
              example, 'ml.p2.xlarge'.
          endpoint_name (str): The name of the endpoint to create (default: None).
              If not specified, a unique endpoint name will be created.
          tags (list[dict]): List of tags for labeling a training job. For more,
              see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
  
      Returns:
          dict: Deploy config that can be directly used by
          SageMakerEndpointOperator in Airflow.
      """
      model_base_config = model_config(model, instance_type)
  
      production_variant = sagemaker.production_variant(
          model.name, instance_type, initial_instance_count
      )
      name = model.name
      config_options = {
          "EndpointConfigName": name,
          "ProductionVariants": [production_variant],
      }
      if tags is not None:
          config_options["Tags"] = tags
  
      endpoint_name = endpoint_name or name
      endpoint_base_config = {
          "EndpointName": endpoint_name,
          "EndpointConfigName": name,
      }
  
      config = {
          "Model": model_base_config,
          "EndpointConfig": config_options,
          "Endpoint": endpoint_base_config,
      }
  
      # if there is s3 operations needed for model, move it to root level of config
      s3_operations = model_base_config.pop("S3Operations", None)
      if s3_operations is not None:
          config["S3Operations"] = s3_operations
  
      return config
  
imports: from __future__ import print_function, absolute_import
  import os
  import re
  import sagemaker
  from sagemaker import fw_utils, job, utils, s3, session, vpc_utils
  from sagemaker.amazon import amazon_estimator
  from sagemaker.tensorflow import TensorFlow
  from sagemaker.estimator import EstimatorBase
  from sagemaker.processing import Processor
  from sagemaker.utils import format_tags
label_desc: Use image node_id 17 for calling a global function with example usage: deploy_config(model, initial_instance_count, instance_type, endpoint_name, tags) and returns: config